Description Plants of the future are expected to be able to quickly detect and characterize anomalies that can prevent product quality. Recently developed learning algorithms and complex systems analysis techniques can be successfully applied to this purpose specially in quality-driven manufacturing processes that are consistently monitored and for which a large amount of process control data are available.

The main objective of this thesis is to design a framework for anomalies detection in industrial plants. More specifically, exploiting data coming from process control technologies, the target is to fully characterize anomalies by correlating with operating conditions and with external factors or environmental conditions. The final purpose is to build an anomaly characterization framework, that will provide information about the possible causes of the anomaly as well as the dependencies between the controlled process and external factors (i.e. different from controlled parameters). The outcome of the framework can be used also for predictive maintenance and process control optimization.